During training, minibatches are sampled so that examples of success and failure are balanced on average. This is crucial because in the beginning, when there are much fewer positive examples than negative examples and the dataset is imbalanced, the model may converge to a suboptimal solution in which all values in the output are close to 0. This technique is also used in other work with similar problems [1]}, [2]}. We use a similar strategy for balancing data across different tasks.
